Sales & inventory data for a fictitious chain of toy stores in Mexico called Maven Toys, including information about products, stores, daily sales transactions, and current inventory levels at each location
# Top category by sales
# Join sales and products
sales_products <- left_join(sales, products, by = "Product_ID")
# Calculate total sales by category
by_category <- sales_products |>
group_by(Product_Category) |>
summarise(Total_Sales = sum(Units * Product_Price)) |>
arrange(desc(Total_Sales))
# Create bar chart with ggplot2
p <- ggplot(by_category, aes(x = fct_reorder(Product_Category, Total_Sales), y = Total_Sales, fill = Product_Category, text = paste(Product_Category, "Total Sale: ", scales::dollar(Total_Sales)))) +
geom_bar(stat = "identity") +
geom_text(aes(label = scales::dollar(Total_Sales), y = Total_Sales), vjust = -0.5, color = "black", size = 3) +
labs(title = "Top Ten Categories by Sales", x = "Product Category", y = "Sales") +
scale_y_continuous(labels = scales::dollar_format()) +
theme(legend.position = "none")
# Convert to Plotly
p_plotly <- ggplotly(p, tooltip = "text")
p_plotlyThe chart shows how much revenue each product category made. Toys made the most revenue, more than $5.09 million. Sports & Outdoors products made 2.17 million revenue, which is the least of all.
# Cost of category
total_cost <- sales_products |>
group_by(Product_Category) |>
summarise(Cost = sum(Product_Cost))
viz <- ggplot(total_cost, aes(x = reorder(Product_Category, Cost), y = Cost, fill = Product_Category, text = paste("Toatl Cost: ", scales::dollar(Cost))))+
geom_bar(stat = "identity")+
geom_text(aes(label = scales::dollar(Cost), y = Cost), vjust = -0.5, color = "black", size = 3) +
labs(title = "Total Cost by Each Category", y= "Cost", x= "Category")+
scale_y_continuous(labels = scales::dollar_format())+
theme_minimal()+
theme(legend.position = "none")
cost_viz <- ggplotly(viz, tooltip = "text")
cost_vizsales_products <- sales_products |>
mutate(Profit = (Product_Price - Product_Cost) * Units)
total_profit <- sales_products |>
group_by(Product_Category) |>
summarise(Profits = sum(Profit))
viz <- ggplot(total_profit, aes(x = reorder(Product_Category, Profits), y = Profits, fill = Product_Category, text = paste("Total Profit: ", scales::dollar(Profits)))) +
geom_bar(stat = "identity") +
geom_text(aes(label = scales::dollar(Profits), y = Profits), vjust = -0.5, color = "black", size = 3) +
labs(title = "Total Profit by Each Category", y= "Profit", x= "Category") +
scale_y_continuous(labels = scales::dollar_format()) +
theme_minimal() +
theme(legend.position = "none")
profit_viz <- ggplotly(viz, tooltip = "text")
profit_viz# Summarize sales by location type
sales_stores <- sales_stores |>
mutate(Profit = (Product_Price - Product_Cost) * Units)
sales_by_location <- sales_stores %>%
group_by(Store_Location) %>%
summarize(Total_Sales = sum(Units * Product_Price),
Total_Costs = sum(Units * Product_Cost),
Total_Profits = sum(Profit))
# Calculate profit margin
sales_by_location <- sales_by_location %>%
mutate(Profit_Margin = Total_Profits / Total_Sales)
# Melt the data for better visualization
sales_melted <- sales_by_location %>%
pivot_longer(cols = c(Total_Sales, Total_Costs, Total_Profits, Profit_Margin), names_to = "Metric", values_to = "Value")
# Specify the order of the levels for the "Metric" variable
metric_order <- c("Total_Profits", "Total_Costs", "Total_Sales", "Profit_Margin") # Adjusted order
sales_melted$Metric <- factor(sales_melted$Metric, levels = metric_order)
# Plotting with ggplot2
sales_plot <- ggplot(sales_melted, aes(x = Store_Location, y = Value, fill = Metric, text = paste(Metric, ": ", ifelse(Metric == "Profit_Margin", scales::percent(Value), scales::dollar(Value))))) +
geom_bar(stat = "identity", position = "stack", color = "white") +
labs(title = "Sales Analysis By Location Type", y = "Amount", x = "Store Location") +
scale_fill_manual(values = c("#8da0cb", "#fc8d62", "#66c2a5", "#e78ac3"), name = "Metric") +
theme_minimal() +
geom_text(position = position_stack(vjust = 0.5), aes(label = ifelse(Metric == "Profit_Margin", scales::percent(Value), scales::dollar(Value))), color = "black", size=3)
interactive_plot <- ggplotly(sales_plot, tooltip = "text")
interactive_plotDowntown leads in both total sales, reaching $8.21M, and profits, totaling about $2.25M, highlighting its strong performance. Commercial follows with $3.2M in sales and $927K in profits. However, the Airport lags behind with the lowest sales at $1.3M. To maximize profits, focus marketing efforts on Downtown, assess Commercial’s cost structure, and evaluate the performance of the struggling Airport location for potential improvements or closure. Additionally, aim to enhance profitability margins; Downtown and Commercial boast 27.36% and 28.27% respectively, while Airport and Residential show room for improvement at 29.31% and 27.80%.
# Top 10 Stores By Total Sales
sales_by_store <- sales_stores |>
group_by(Store_Name) |>
summarise(totalsales = sum(Product_Price * Units)) |>
arrange(desc(totalsales)) |>
slice_head(n = 10)
# Visualization
plot_sales_by_store <- ggplot(sales_by_store, aes(x =fct_reorder(Store_Name, totalsales), y=totalsales, fill = Store_Name, text=paste("Total Sales: ", scales::dollar(totalsales)))) +
geom_bar(stat = "identity") +
geom_text(aes(label = scales::dollar(totalsales), y = totalsales), vjust = -0.5, color = "black", size = 3) +
labs(title = "Top Ten Stores by Sales", x = "Store Name", y = "Sales") +
scale_y_continuous(labels = scales::dollar_format()) +
theme_minimal() +
theme(legend.position = "none")+
coord_flip()
# Interactive plot
interactive_visuales <- ggplotly(plot_sales_by_store, tooltip = "text")
interactive_visualesThe chart shows which store is generation the most revenue.
# Daily Average Units Solds per Product
# Extract date
sales_products <- sales_products |>
mutate(Date = as.Date(Date))
# Units sold per product per day
daily_sales <- sales_products |>
group_by(Product_ID, Date) |>
summarise(Units = sum(Units))
# Average daily units sold
avg_daily <- daily_sales |>
group_by(Product_ID) |>
summarise(Avg_Daily_Units = mean(Units))
# Plotting the average daily units sold
avg_daily_plot <- ggplot(avg_daily, aes(x = reorder(Product_ID, Avg_Daily_Units), y = Avg_Daily_Units, text=paste("Product ID: ", Product_ID," Sold ", round(Avg_Daily_Units,1), " Units Daily"))) +
geom_point(size = 3, color = "yellow") +
geom_text(aes(label = scales::number(round(Avg_Daily_Units, 1))), vjust = -0.5, size = 3) +
labs(title = "Average Daily Units Sold per Product", x = "Product ID", y = "Average Daily Units Sold") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
interactive_avg_daily_plot <- ggplotly(avg_daily_plot, tooltip = "text")
interactive_avg_daily_plotThe chart demonstrate the average number of units of each product sold per day * Product_ID = 3 is the top sold product with 217.7 units sold daily on average * And Product_ID 20 and 35 are the at the bottom.
# Daily Total Sales
# Extract date
sales_stores <- sales_stores |>
mutate(Date = as.Date(Date))
# Daily sales
daily_sales <- sales_stores |>
group_by(Date) |>
summarize(Daily_Sales = sum(Units * Product_Price))
# Plotting daily sales
daily_sales_plot <- ggplot(daily_sales, aes(x = Date, y = Daily_Sales, text = paste("Total Sale on ",Date, "Was", scales::dollar(Daily_Sales)))) +
geom_line(aes(group = 1), color = "blue", size = 2) + # Include the group parameter
geom_point(color = "black", size = 2) +
labs(title = "Daily Sales Over Time", x = "Date", y = "Daily Sales") +
theme_minimal()
interactive_chart <- ggplotly(daily_sales_plot, tooltip = "text")
interactive_chart# Week_Days Sales Trends
# Extract weekday
sales_stores <- sales_stores |>
mutate(Weekday = wday(Date, label=TRUE))
# Sales by weekday
sales_by_wkday <- sales_stores |>
group_by(Weekday) |>
summarize(Sales = sum(Units*Product_Price))
# Plotting sales by weekday
sales_by_wkday_plot <- ggplot(sales_by_wkday, aes(x = Weekday, y = Sales, text=paste("Total Sale on: ", Weekday, "is ", scales::dollar(Sales)))) +
geom_bar(stat = "identity", fill="skyblue") +
labs(title = "Sales by Weekday", x = "Weekday", y = "Sales") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat")) + # Set weekday labels
theme_minimal()
Viz_sales_by_wkday_plot <- ggplotly(sales_by_wkday_plot, tooltip = "text")
Viz_sales_by_wkday_plot###Top 10 Products by Sale###
# Group by Product_ID and summarize sales
prod_sales <- sales_products |>
group_by(Product_ID) |>
summarise(Unit_Solds = sum(Units))
# Arrange descending by sales
prod_sales <- prod_sales |>
arrange(desc(Unit_Solds))
# Take the top 10 rows
top_10 <- head(prod_sales, 10)
# Plotting top products by sales
top_10_plot <- ggplot(top_10, aes(x = reorder(Product_ID, Unit_Solds), y = Unit_Solds, text= paste("Unit Solds: ", Unit_Solds))) +
geom_bar(stat = "identity", fill="#e60049") +
labs(title = "Top 10 Products by Unit Solds", x = "Total Sales", y = "Product ID") +
theme_minimal()
top_10_plot_viz <- ggplotly(top_10_plot, tooltip = "text")
top_10_plot_viz# Most profitable products
sales_products <- sales_products |>
mutate(Profit = (Product_Price - Product_Cost) * Units)
product_profits <- sales_products |>
group_by(Product_ID) |>
summarize(Total_Profit = sum(Profit))
product_profits <- arrange(product_profits, desc(Total_Profit))
Top_Products <- head(product_profits, 10)
# Interactive Viz for the top 10 products
profit_viz <- ggplot(Top_Products, aes(x = Total_Profit, y = reorder(Product_ID, -Total_Profit), text = paste("Total Profit: ", scales::dollar(Total_Profit)))) +
geom_bar(stat = "identity", fill = "skyblue") +
labs(title = "Top 10 Most Profitable Products", x = "Total Profit", y = "Product ID") +
scale_x_continuous(labels = scales::dollar_format()) + # Format x-axis labels as dollars
theme_minimal() +
theme(legend.position = "none") +
coord_flip()
p_plotly <- ggplotly(profit_viz, tooltip = "text")
p_plotly# How do sales fluctuate over the course of a year?
# Extract month and year from date
sales_stores <- sales_stores |>
mutate(Month = lubridate::month(Date),
Year = lubridate::year(Date))
# Aggregate sales by month and year
monthly_sales <- sales_stores |>
group_by(Year, Month) |>
summarise(Sales = sum(Units * Product_Price))
# Plotting monthly sales over the course of a year using a bar chart
monthly_sales_bar_plot <- ggplot(monthly_sales, aes(x = interaction(Year, Month, drop = TRUE), y = Sales, fill =as.factor(Year), text=paste("Total Sales in Month", Month," of Year",Year ,"is", scales::dollar(Sales)))) +
geom_bar(stat = "identity") +
labs(title = "Monthly Sales Over the Year", x = "Month", y = "Sales") +
scale_x_discrete(labels = scales::date_format("%b %Y")) + # Format labels as Month Year
scale_fill_viridis_d() + # Using a color scale for years
theme(axis.text.x = element_text(angle = 45, hjust = 1))
monthly_sales_plot_viz <- ggplotly(monthly_sales_bar_plot, tooltip = "text")
monthly_sales_plot_viz# Top/Bottom stores by Profit
sales_stores <- sales_stores |>
mutate(Profit = (Product_Price - Product_Cost) * Units)
store_profits <- sales_stores |>
group_by(Store_ID) |>
summarize(Total_Profit = sum(Profit))
store_profits <- arrange(store_profits, desc(Total_Profit))
top_stores <- head(store_profits, 5)
bottom_stores <- tail(store_profits, 5)
# Create diverging bar chart for top stores
top_stores_chart <- ggplot(top_stores, aes(x = reorder(Store_ID, -Total_Profit), y = Total_Profit, fill= Store_ID, text=paste("Total Profit: ", scales::dollar(Total_Profit)))) +
geom_bar(stat = "identity") +
labs(title = "Top Stores by Profit", x = "Store ID", y = "Total Profit") +
theme_minimal() +
theme(legend.position = "none")
int_top_stores_chart <- ggplotly(top_stores_chart, tooltip = "text")
int_top_stores_chart#Bottom 10 Stores by Profit
# Create diverging bar chart for bottom stores
bottom_stores_chart <- ggplot(bottom_stores, aes(x = reorder(Store_ID, Total_Profit), y = Total_Profit, fill=Store_ID, text=paste("Total Profit: ", scales::dollar(Total_Profit)))) +
geom_bar(stat = "identity", color = "black") +
labs(title = "Bottom Stores by Profit", x = "Store ID", y = "Total Profit") +
theme_minimal() +
theme(legend.position = "none")
int_bottom_stores_chart <- ggplotly(bottom_stores_chart, tooltip = "text")
int_bottom_stores_chart